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» Solving Sparse Linear Constraints
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TIT
2010
100views Education» more  TIT 2010»
14 years 5 months ago
Theoretical and empirical results for recovery from multiple measurements
The joint-sparse recovery problem aims to recover, from sets of compressed measurements, unknown sparse matrices with nonzero entries restricted to a subset of rows. This is an ex...
Ewout van den Berg, Michael P. Friedlander
106
Voted
ICML
2005
IEEE
15 years 11 months ago
Analysis and extension of spectral methods for nonlinear dimensionality reduction
Many unsupervised algorithms for nonlinear dimensionality reduction, such as locally linear embedding (LLE) and Laplacian eigenmaps, are derived from the spectral decompositions o...
Fei Sha, Lawrence K. Saul
ICDM
2009
IEEE
174views Data Mining» more  ICDM 2009»
15 years 5 months ago
Non-sparse Multiple Kernel Learning for Fisher Discriminant Analysis
—We consider the problem of learning a linear combination of pre-specified kernel matrices in the Fisher discriminant analysis setting. Existing methods for such a task impose a...
Fei Yan, Josef Kittler, Krystian Mikolajczyk, Muha...
ICCS
2007
Springer
15 years 5 months ago
Neural Networks for Predicting the Behavior of Preconditioned Iterative Solvers
We evaluate the effectiveness of neural networks as a tool for predicting whether a particular combination of preconditioner and iterative method will correctly solve a given spar...
America Holloway, Tzu-Yi Chen
83
Voted
COLT
2004
Springer
15 years 4 months ago
Regularization and Semi-supervised Learning on Large Graphs
We consider the problem of labeling a partially labeled graph. This setting may arise in a number of situations from survey sampling to information retrieval to pattern recognition...
Mikhail Belkin, Irina Matveeva, Partha Niyogi